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An ontology-based data fusion framework for profiling sensors

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3 Author(s)
Kothari, C. ; Purdue Sch. of Eng. & Technol., Indiana Univ. - Purdue Univ., Indianapolis, IN, USA ; Qualls, J. ; Russomanno, D.

Data-to-decision systems must fuse information from heterogeneous sources to infer a high-level understanding of a situation. A high degree of confidence in the inferred knowledge is necessary for appropriate actions to be taken based upon the assessment of a situation. This paper presents an extensible Semantic Web compatible framework that uses rich ontological descriptions for the autonomous and human-aided fusion of heterogeneous sensors and algorithms to create evidence-based hypotheses of a situation under persistent surveillance. Raw data acquired from profiling sensors is combined with the output of visualization and classification algorithms, yielding information with a higher degree of confidence than what would be obtained without the fusion process. The framework can readily accommodate other data sources and algorithms into the fusion process.

Published in:

Electro/Information Technology (EIT), 2012 IEEE International Conference on

Date of Conference:

6-8 May 2012